Technical feasibility

SECTION I: Testing

 

1

Has the R & D result been tested?

YES

 

 

1a

In what mode has the result been tested?

                Prototype

                Pilot Application

                Alpha/BETA testing

 

A user-aware virtual museum environment named Virtual Wing is proposed where users, apart from ‘consuming’ content, can express their opinions on it. By providing such an option, we give users an active role which helps them retain their interest, deal with potential boredom and develop the feeling that their actions have an impact. In particular the Virtual Wing creates and presents a complete virtual representation of a museum, providing rich multimedia descriptions for its content. A user-aware virtual museum which has been developed and tested  for the Archaeological Museum of Volos.

b.

Please describe and discuss the testing results

As it was previously described, ratings, comments and page hits are used to calculate the most popular content for each users’ group. In order to create the list of items for each group of users, the feedback data is divided into three subgroups consisting of the feedback given by each group. Then, 20% of the available data is kept from each kind of feedback with priority given to the date it was provided. At this point, we have the most recent 20% of all comments, ratings, and page hits, regardless of the objects to which they may belong. Then we apply the following formula:

PSi = (RAi−RA)×a+(PHi − PH )×b+(Ci − C )×c PH n×PH C n×C

where PSi is the popularity score of i-th item, RA is the average rating of all items, RAi is the average rating of the i-th item, PH is the number of page hits of all the items while PHi is the number of page hits of the i-th item, C is the overall number of comments, Ci is the number of comments on the i-th item and, finally, n is the total number of items. Also, a, b and c are the relative weights we assign respectively to ratings, page hits and comments. The above constants comply by the following formula:

a + b + c = 1, c > a > b

We give greater weight to comments and then ratings since experience has shown that users tend to rate more frequently than comment since it requires significantly less time and ef- fort. Therefore, commenting is considered evidence of stronger interest than rating.

The above formula assigns a score to each item, based on how many comments and page hits it has received, as well as its overall rating. The formula was created taking into consideration all three types of feedback in order to avoid cold-start problems and grey sheep phenomena, which are encountered when a new item is added, as well as avoid overfitting of the group’s model. The former is achieved mainly by the fact that we use 20% of the latest data and not the entire population, allowing new exhibits that only recently caught the attention of the visitors to be included in the list. On top of that, an initial build of momentum is more easily achieved due to the inclusion of page hits as an interest indicator. It is commonly known that a user may view many items but will not rate or comment in most cases. Additionally, all measurements have been normalized to further avoid the problem of overfitting and instead of using all of the data, only the latest 20% according to the submission date are used. This method enables us to effectively detect trends and rising interest from a particular group for certain items.

 

 

SECTION 2: Current Stage of Development

 

2a

To what extent does the development team have technical resources for supporting the production of a new product? (Researchers, human resources, hardware, etc. )

 

OSWINDS group has carried out work on Web data mining with emphasis on Web 2.0 social networks, such as Social Tagging Systems, studying the :
(i) dynamics driving the generation and popularity of content in social networks, 
(ii) representation and storage of relationships between entities, such as users, resources, and metadata, with appropriate models (such as graphs, communities etc). 
(iii) detection algorithms for uncovering implicit groups of entities (users, tags, or combinations of entities) with similar characteristics or behaviors (e.g. tags describing same concepts, users sharing common interests etc) 
(iv) identification of user behavioral and emotional patterns emerging on a social networking application (in collaboration with colleagues from Dept of Psychology in Crete).
Recent efforts have focused on social applications mining with a focus on evolving information streams, as they emerge from particular microblogging activities.
Microblogging (among social networks) offers an obvious ground for expressing opinions, and views. Twitter has become a majorly popular application and certainly its relationships (followers, referencing, etc) embed human behavioral and emotional actions. The real anytime and anywhere posting of information offers a wide range of opportunities for detecting trends, analyzing opinions and emotions, and finally capturing the so-called “wisdom of the crowds”.

 

2b

What are the technical issues that need to be tackled for full deployment, if needed?

 

Both the group-based logic of operations as well as the monitoring of the users’ activities in the system can be used in a number of ways to improve users’ experience in the museum. Two ideas are presented below. The first one is used to further improve the virtual tour provided to visitors, while the second is used to improve the museum administrators’ job of collecting data from visitors.

 

 

2c

What additional technical resources are needed for the production of this new product?

 

A web programmer for improving the virtual tour services

A database programmer to improve data collections from visitors

 

 

 

2d

Overall assessment of the current stage of technical development.

Moreover, it models both associa- tions and common features between artifacts and entities that may be abstract or reside outside the museum (e.g. eras and archaeological sites, respectively) through specialized filtering and group-presentation capabilities. Visitors are free to explore the virtual museum’s world having constant assistance during their navigation. Furthermore, the Virtual Wing provides user interfaces tailored to the needs of different user groups.

 

SECTION 3: Deployment

3a

Define the demands for large scale production in terms of

·       Materials

The is no need for mass production since the services will be delivered over web.

·       technologies, tools, machineries

 

·       Staff effort

 

 

SECTION 4: Overall Assessment

1

What is you overall assessment of the technical feasibility of the research result?

In contrary to existed related systems, the tool offers an active role to users which are not just content ‘consumers’ but they also contribute to the system by providing valuable feedback. Users can show their preferences on the exhibits by rates, express their viewpoints by posting comments on them, and provide their general opinions about the system itself through questionnaires. The analysis of all this provided information may lead to useful outcomes for museum administrators in terms of content popularity and the system’s services. Therefore, we believe that with the proposed system, virtual museums can take one step ahead in the ultimate objective of providing better user experience.

 

KEYWORDS QUANTITATIVE ASSESSMENT (0-5).

 

Please put X as appropriate.

1

2

3

4

5

Adequacy of testing activity undertaken so far

 

 

 

 

x

Adequacy and availability of technical resources of the development team

 

 

 

 

x

Current development stage

 

 

 

 

x

Overall technical feasibility

 

 

 

 

x

 

 

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